A case study on experimental-data validation for natural language processing
Dongli Han, Takahiro Ohno · 2016
Text data randomly extracted from a particular corpus are usually employed by NLP (Natural language processing) systems as experimental data. However, it is hard to determine whether the experimental data is appropriate without a reasonable validation process. We in this paper describe a data validation approach for a NLP-based e-learning system we have built before.